import datetime

import psycopg2
from pymongo import MongoClient
from co2_emission.co2_emission import Co2Emission
from costs.cost import Cost
from building import Building
from city_object import CityObject
from hub.catalog_factories.energy_systems_catalog_factory import EnergySystemsCatalogFactory


def calculate_building(building_info, building_results, scenario_id):
  energy_systems_catalog = EnergySystemsCatalogFactory('montreal_custom').catalog
  archetype = energy_systems_catalog.get_entry(building_info.system_name)
  mockup_building = Building(building_info, building_results, archetype)
  life_cycle = Cost(mockup_building, retrofit_scenario=scenario_id).life_cycle
  operational_co2 = Co2Emission(mockup_building).operational_co2
  global_capital_costs = life_cycle[f'Scenario {scenario_id}']['global_capital_costs']
  global_operational_costs = life_cycle[f'Scenario {scenario_id}']['global_operational_costs']
  global_capital_incomes = life_cycle[f'Scenario {scenario_id}']['global_capital_incomes']
  global_maintenance_costs = life_cycle[f'Scenario {scenario_id}']['global_maintenance_costs']
  building_results['total_heating_area'] = building_info.total_heating_area
  building_results['year_of_construction'] = building_info.year_of_construction
  building_results['function'] = building_info.function
  building_results['costs'] = {
    'total_capital_costs_skin': life_cycle[f'Scenario {scenario_id}']['total_capital_costs_skin'],
    'total_capital_costs_systems': life_cycle[f'Scenario {scenario_id}']['total_capital_costs_systems'],
    'end_of_life_costs': life_cycle[f'Scenario {scenario_id}']['end_of_life_costs'],
    'total_operational_costs': life_cycle[f'Scenario {scenario_id}']['total_operational_costs'],
    'total_maintenance_costs': life_cycle[f'Scenario {scenario_id}']['total_maintenance_costs'],
    'operational_incomes': life_cycle[f'Scenario {scenario_id}']['operational_incomes'],
    'capital_incomes': life_cycle[f'Scenario {scenario_id}']['capital_incomes'],
    'global_capital_costs': {
      'B2010_opaque_walls': global_capital_costs['B2010_opaque_walls'].tolist(),
      'B2020_transparent': global_capital_costs['B2020_transparent'].tolist(),
      'B3010_opaque_roof': global_capital_costs['B3010_opaque_roof'].tolist(),
      'B10_superstructure': global_capital_costs['B10_superstructure'].tolist(),
      'D3020_heat_generating_systems': global_capital_costs['D3020_heat_generating_systems'].tolist(),
      'D3030_cooling_generation_systems': global_capital_costs['D3030_cooling_generation_systems'].tolist(),
      'D3080_other_hvac_ahu': global_capital_costs['D3080_other_hvac_ahu'].tolist(),
      'D5020_lighting_and_branch_wiring': global_capital_costs['D5020_lighting_and_branch_wiring'].tolist(),
      'D301010_photovoltaic_system': global_capital_costs['D301010_photovoltaic_system'].tolist(),
    },
    'global_end_of_life_costs': life_cycle[f'Scenario {scenario_id}']['global_end_of_life_costs'][
      'End_of_life_costs'].tolist(),
    'global_operational_costs': {
      'fixed_costs_electricity_peak': global_operational_costs['Fixed_costs_electricity_peak'].tolist(),
      'fixed_costs_electricity_monthly': global_operational_costs['Fixed_costs_electricity_monthly'].tolist(),
      'variable_costs_electricity': global_operational_costs['Variable_costs_electricity'].tolist(),
      'fixed_costs_gas': global_operational_costs['Fixed_costs_gas'].tolist(),
      'variable_costs_gas': global_operational_costs['Variable_costs_gas'].tolist()
    },
    'global_maintenance_costs': {
      'heating_maintenance': global_maintenance_costs['Heating_maintenance'].tolist(),
      'cooling_maintenance': global_maintenance_costs['Cooling_maintenance'].tolist(),
      'pv_maintenance': global_maintenance_costs['PV_maintenance'].tolist(),
    },
    'global_operational_incomes': life_cycle[f'Scenario {scenario_id}']['global_operational_incomes'][
      'Incomes electricity'].tolist(),
    'global_capital_incomes': {
      'subsidies_construction': global_capital_incomes['Subsidies construction'].tolist(),
      'subsidies_hvac': global_capital_incomes['Subsidies HVAC'].tolist(),
      'subsidies_pv': global_capital_incomes['Subsidies PV'].tolist()
    }
  }
  building_results['operational_co2'] = operational_co2


start = datetime.datetime.now()
scenario_ids = {'current status': 0,
                'skin retrofit': 1,
                'system retrofit and pv': 2,
                'skin and system retrofit with pv': 3
                }

connection = psycopg2.connect(database='montreal_retrofit', user='retrofit', password='C3rcIT$vc#', host='127.0.0.1',
                              port=5432)

cursor = connection.cursor()

client = MongoClient('mongodb://localhost:27017/')
montreal_retrofit_db = client.montreal_retrofit
meb_collection = montreal_retrofit_db.me

cursor.execute("SELECT id, scenario FROM city where scenario <> 'current status';")

cities = cursor.fetchall()

for city in cities:
  cursor.execute(
    f'SELECT DISTINCT id, name, aliases, year_of_construction, function, usage, volume, area, total_heating_area, '
    f'wall_area, windows_area, roof_area, total_pv_area, system_name FROM city_object WHERE city_id = {city[0]}')
  buildings = cursor.fetchall()
  for building in buildings:
    cursor.execute(f'SELECT values FROM simulation_results WHERE city_object_id = {building[0]};')
    values = cursor.fetchall()
    for value in values:
      building_data = {'city_id': city[0], 'name': building[1], 'alias': building[2].split(',')[0][1:],
                       'year_of_construction': building[3],
                       'function': building[4], 'usage': building[5], 'volume': building[6], 'area': building[7],
                       'total_heating_area': building[8], 'wall_area': building[9], 'windows_area': building[10],
                       'roof_area': building[11], 'total_pv_area': building[12], 'system_name': building[13],
                       'scenario': city[1]}
      city_object = CityObject(building_data)
      results = values[0][0]
      try:
        calculate_building(city_object, results, scenario_ids[city[1]])
      except KeyError:
        pass
      building_data['insel meb'] = results['insel meb']
      building_data['costs'] = results['costs']
      building_data['operational_co2'] = results['operational_co2']
      try:
        montreal_retrofit_db[f'meb_{city[1].replace(" ", "_")}'].insert_one(building_data)
      except:
        pass

print(f'Completed in: {datetime.datetime.now() - start}')